Research >> UCLA-NIST Smart Manufacturing Study

UCLA-NIST Smart Manufacturing Study

Objective & Approach

This project defined and developed, in Year 1, the common workflow-based data management, modeling and simulation and performance metric architecutres drives for smart machine benchmarking.  Drafted initial functional requirements for an architecutre and idenitied major gaps and barriers.  The nature of machine benchmarking is expected to result in a focus on defining the architectural drivers for machine and process level data collection and management, use of the data for dynamic management and adaptability of the in-production use and peformance of the machine and defnition of machine and process level performance metric components and measurements.

In years 2 and 3, definition and development of common data management modeling and simulation and performance metric architectural drivers for in-production integrated computational materials engineering (ICME) and rapid qualification based on industry problem examples. Extend the functional requirements for the platform architecture to include the application of high fidelity modeling for in-production, real-time use and identified gaps and barriers. Validate common architectural drivers for all areas.

Details

SPONSOR / PARTNER
Department of Commerce

YEAR
2016

TOPIC
UCLA NIST Smart Manufacturing Study

PRINCIPAL INVESTIGATOR
Brian Schott

PRINCIPAL-IN-CHARGE
Robert Graybill

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